Negation recognition in medical narrative reports

Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. When searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. Hence, negation is a major source of poor precision in medical information retrieval systems. Previous research has shown that negated findings may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We present a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy. The new algorithm can be applied also to further context identification and information extraction tasks.